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Abstract:
We propose the framework of mutual information kernels for
learning covariance kernels, as used in Support Vector machines
and Gaussian process classifiers, from unlabeled task data using
Bayesian techniques. We describe an implementation of this
framework which uses variational Bayesian mixtures of factor
analyzers in order to attack classification problems in
high-dimensional spaces where labeled data is sparse, but
unlabeled data is abundant.
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